Abstract
For the category-imbalanced applications, traditional ensemble data stream mining algorithms will result in low accuracy for the small classes and fail to meet the needs of applications. This paper provides a novel class-imbalanced data learning method based on MAE named CIMAE to solve the above problem. Instead of directly using each incoming data, it acquires data blocks for online training each time by setting up a sample library and a sliding window. Compared with traditional data stream mining algorithms, the results showed that CIMAE achieves the state-the-of-art performance for class-imbalanced application.
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Acknowledgement
This work is sponsored by the National Key Research and Development Program of China (2017YFC0806502, 2017YFC0803700, 2017YFC0821600) and by the Shanghai Rising-Star Program (17QB1401000) and by the Application Innovation Plan of Ministry of Public Security (2017YYCXSXST030) and by the program of Science and Technology Commission of Shanghai municipality (Nos. 15530701300, 1759800900).
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Sun, Q., Wu, Y., Duan, H., Wang, J., Mei, L. (2019). An Ensemble Data Stream Mining Algorithm for Class-Imbalanced Applications. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Security and Intelligence ATCI 2018. ATCI 2018. Advances in Intelligent Systems and Computing, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-319-98776-7_12
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DOI: https://doi.org/10.1007/978-3-319-98776-7_12
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